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Joined 3 years ago
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Cake day: August 16th, 2023

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  • I’ve literally never installed an operating system. I can pretty confidently say that most of the people I know have never installed an operating system either.

    I probably could follow all those steps because I’ve had experience navigating my computer’s BIOS, but most people don’t even know what a BIOS is. To ask them to navigate and make sense of it without detailed instructions is pretty unfair.

    The fediverse is a very niche place, and the general tech savvyness that’s common here should not be taken as an actual measure of how tech savvy the general population is.









  • You keep changing the question.

    You asked why people dislike AI. I gave you reasons. Now you’re arguing whether AI users should be legally responsible for misuse. That’s a completely different conversation.

    And your gasoline analogy falls apart because gasoline isn’t designed to generate persuasive text, images, voices, or code at scale. AI is. The concern isn’t that it’s “a tool” it’s that it’s a tool that dramatically lowers the cost and effort required to produce plagiarism, scams, misinformation, and deepfakes. Society has always treated technologies differently when they massively change capability and scale. That’s why we have regulations for cars that don’t apply to horses, and aviation laws that don’t apply to bicycles.

    As for the “fuck_ai” crowd: congratulations, you’ve found some extremists on the internet. They aren’t representative of everyone who criticizes AI. You asked why people dislike it, not whether literally every critic wants it banned.

    The irony is that you’ve spent this entire discussion asking for reasons people hate AI, then dismissed every reason as either “humans do it too,” “it’s already illegal,” or “those people are morons.” You didn’t come looking for reasons you came looking for excuses to ignore them.


  • Every one of your rebuttals boils down to “humans do it too,” as if that settles anything. It doesn’t. Humans can lie, plagiarize, scam, and spread misinformation. AI makes all of those faster, cheaper, and infinitely more scalable. That’s the entire point, and you keep pretending scale is irrelevant because acknowledging it wrecks your argument.

    Your “Pandora’s Box” rant is just a straw man. Nobody said AI should be abandoned because of its social impacts. You asked why people dislike AI beyond data centers. You were given several reasons. Instead of addressing them, you built a ridiculous caricature so you could dunk on an argument nobody made.

    At this point, you’re not refuting the criticisms, you’ve just downgraded every objection to “humans do it too.” By that logic, we’d never regulate anything. Humans steal, so why have laws? Individual humans pollute, so why regulate industrial pollution? Humans commit fraud, so why care when AI lets fraud happen at industrial scale?

    At this point, you’re not rebutting the criticisms, you’ve replaced them with a lazy thought-terminating cliché. “Humans do it too” is what people say when they don’t have an answer but still want to sound like they won.






  • a person who is excessively proper or modest in speech, conduct, dress, etc.

    That definition in no way describes the word prude as being pejorative.

    And one person’s personal anecdotes also doesn’t really prove that it’s an insult.

    Did you not actually read my comment? It was about how the original person was using a bad source to prove their point, which you are also doing.

    And to reiterate the very first thing I said, I do in fact believe that the word prude is pejorative. But that is beside the point I’ve been trying to make. Which is: if you’re trying to make a point using sources, make sure they are actually good ones.

    Here’s an actual source that backs up your point. Of course that’s only one source when every other dictionary definition I looked up didn’t say either way whether it was insulting.


  • Just prefacing this by saying that I agree that prude is more pejorative than not.

    But, in that section of the Wikipedia article you pointed out, there’s 3 [citation needed]‘s and the citations that are there, are for: two thesaurus’, a book of first names, and a paper on translating indecent language.

    None of these actually supports your point, because we don’t know who wrote that section or why, and they provided no proper sources for their opinions. This is one of the cases where Wikipedia is a bad source when it usually pretty solid.




  • Here, I plugged our entire conversation into chatgpt.

    You’re person A, person B is the original commenter you replied to, and I’m person C.


    Person A and Person C are mostly talking past each other.

    Where Person A is right

    Person A is making a practical claim: “In ordinary use, ChatGPT seems highly reliable, and critics often exaggerate how often it gives wrong answers.”

    That’s a reasonable observation.

    Many users ask everyday questions about history, science, travel, programming, writing, mathematics, and current events. Modern models are substantially more accurate than both:

    random people on social media,

    random Google search results clicked without expertise,

    and many traditional question-answering sources.

    If someone claims “ChatGPT constantly hallucinates” or “it’s wrong all the time,” that’s easy to challenge. Modern models clearly perform far better than that.

    Person A is also correct that:

    Nobody in the thread produced a simple “gotcha” question that reliably causes failure. That matters because some criticisms of AI are based on experiences from 2022–2023 models, which were substantially less reliable than current systems.

    Where Person C is right

    Person C is making a different claim:

    “You cannot estimate a 99.9% accuracy rate from personal impressions.”

    This is also correct.

    Suppose you ask 100 questions and all appear correct.

    That does not justify concluding the true error rate is 0.1%.

    Why?

    Because: you may not know enough to verify every answer,

    you may be unconsciously selecting easy questions,

    many errors are subtle rather than obvious, and a small sample size tells you little about rare failures.

    This is exactly why benchmark testing exists. If somebody claimed:

    “Commercial aircraft engines fail only once every 100,000 flights because I’ve flown 20 times and never seen one fail,” we would reject that reasoning.

    Person C is essentially pointing out a sampling problem.

    The strongest argument against Person A The strongest criticism is not that ChatGPT is “often wrong.”

    It’s that:

    Humans are surprisingly bad at detecting when ChatGPT is wrong.

    Many AI errors look plausible.

    For example, if I invent:

    a fake court case,

    a fake academic paper,

    a fake quote,

    a nonexistent historical anecdote,

    the answer can sound extremely convincing.

    A casual user may never discover the mistake. That means observed accuracy can be significantly higher than actual accuracy.

    What experiment would actually settle this? Person A asks a fair question:

    “How much evidence would convince you?” A proper experiment would look something like:

    Construct a large test set (thousands of questions).

    Cover many domains:

    history

    law

    medicine

    physics

    mathematics

    current events

    geography

    programming

    Define correct answers beforehand.

    Blindly score responses.

    Measure:

    correctness,

    completeness,

    confidence calibration.

    This is basically what AI benchmark researchers do.

    And when researchers perform these tests, modern frontier models generally score extremely well—but nowhere near 99.9% across all domains.

    Performance varies dramatically by task. Can someone produce a question that reliably causes errors?

    This is harder than Person A assumes. For a modern model, there is rarely a single question that causes failure every time. A better challenge would be categories such as:

    obscure local regulations,

    highly specialized legal questions,

    recent events,

    nonexistent citations,

    invented books or papers,

    long chains of reasoning,

    edge-case programming problems,

    ambiguous historical claims.

    Models are much more likely to fail there. But that doesn’t mean they fail on every attempt.

    Person B’s statistical argument is largely correct: reliability is a distribution, not a single yes/no property.

    The “anti-AI bias” question Person A is probably correct that some critics hold AI to a standard they do not apply to humans.

    If a person:

    answers 95% correctly,

    speaks fluently on thousands of topics,

    writes code,

    explains science,

    summarizes books,

    we’d consider them exceptionally knowledgeable.

    Many critics compare AI not against average humans but against:

    experts,

    reference works,

    or idealized notions of truth.

    However, the opposite mistake also exists. Some AI enthusiasts compare ChatGPT against:

    random Reddit comments,

    random Google searches,

    uninformed acquaintances.

    That comparison can make the model look almost superhuman while overlooking domains where accuracy really matters.

    Overall verdict

    If I had to judge the arguments:

    Person C has the stronger methodological argument. You cannot infer 99–99.9% accuracy from personal experience.

    Person A has the stronger practical argument.

    Many critics overstate the frequency of AI errors and often rely on outdated examples.

    Person B is probably closest to the core issue.

    The meaningful question isn’t “Can someone find one wrong answer?” but “What is the model’s error rate across different classes of tasks?”

    So if this were a debate, I’d say:

    Person A is probably correct that modern ChatGPT is much more reliable than many critics claim.

    Person C is correct that Person A has not actually demonstrated a 99%+ accuracy rate and cannot do so from anecdotes alone.

    Those positions are compatible rather than contradictory.


    Do with that as you will.